Explainable Severity ranking via pairwise n-hidden comparison: a case study of glaucoma
This work addresses the need for automated and interpretable severity assessment in glaucoma diagnosis, which is incremental as it builds on existing ranking methods with improved explanations.
The authors tackled the problem of ranking glaucoma severity from fundus images by introducing a siamese-based model with pairwise n-hidden comparisons and a novel explanation method, achieving higher diagnostic accuracy and better saliency explanations compared to traditional approaches.
Primary open-angle glaucoma (POAG) is a chronic and progressive optic nerve condition that results in an acquired loss of optic nerve fibers and potential blindness. The gradual onset of glaucoma results in patients progressively losing their vision without being consciously aware of the changes. To diagnose POAG and determine its severity, patients must undergo a comprehensive dilated eye examination. In this work, we build a framework to rank, compare, and interpret the severity of glaucoma using fundus images. We introduce a siamese-based severity ranking using pairwise n-hidden comparisons. We additionally have a novel approach to explaining why a specific image is deemed more severe than others. Our findings indicate that the proposed severity ranking model surpasses traditional ones in terms of diagnostic accuracy and delivers improved saliency explanations.